Abstract
In 2022, 4,764 fatalities in the United States involved heavy-duty trucks, with 66% of these deaths affecting passenger vehicle occupants. Many resulted from underride collisions, where smaller vehicles slide under the high-clearance sides or rear of trailers, often causing catastrophic outcomes. Despite the severity of this issue, the transportation industry lacks a clear, standardized definition of “smart trailers” and guidelines for evaluating and mitigating such risks. This absence hinders the development of cohesive safety policies, the adoption of effective safety measures, and the creation of implementation roadmaps. Unlike Europe, where side underride guards are mandated and proven effective, the U.S. has yet to establish similar frameworks, contributing to uncertainty for policymakers, fleet managers, and technology developers.
This project addresses these challenges by clarifying the definition of smart trailers and establishing a data-driven framework to evaluate their safety, efficiency, and economic benefits. Smart trailers integrate advanced sensors, telematics, and artificial intelligence (AI) to monitor operations, predict risks, and optimize maintenance schedules. A key focus is determining the direct and indirect costs and benefits of equipping trailers with side and rear underride guards and evaluating proactive warning systems or additional technical and policy solutions to reduce trailer-related collisions. Tackling the limited real incident data, the study will integrate data-driven analysis with physics-informed machine learning to guide policymakers in developing practical, safety-enhancing regulations.
To achieve these objectives, the project will synthesize data and physics-based models from multiple sources, including accident reports, vehicle physics models, historical crash data, regulatory documents, and insurance industry records. This information will support the development of simulation-driven models to assess both the safety and economic implications of underride guards or alternative solutions. By leveraging these models, the research will provide actionable insights into the optimal design and implementation of safety measures and how existing smart trailer technologies can support evidence-based regulatory decisions.
Collaboration with key partners will ensure the research's practical relevance and impact. Clarience Technologies will evaluate current smart trailer technologies, identify limitations, and help define a standardized framework. Safety Emissions Solutions will contribute inspection and regulatory data to enhance cost-benefit analyses, while the Regional Industrial Development Corporation (RIDC) will assess how smart trailer technologies can address regional transportation challenges and resource allocation challenges. Pitt Ohio’s participation in validating the system through real-world testing and operational data collection is tentative and subject to confirmation.
The outcomes of this project include a standardized definition of smart trailers, recommendations for safety policy, and a framework for adopting advanced safety technologies. By addressing critical gaps in definitions, data, and evaluation methods, this research will guide the development of effective regulations and implementation roadmaps. Through collaboration with industry leaders, this project aims to enhance public safety, operational efficiency, and sustainable fleet management, paving the way for a safer and more technologically integrated future in heavy-duty transportation.
Description
Timeline
Strategic Description / RD&T
Section left blank until USDOT’s new priorities and RD&T strategic goals are available in Spring 2026.
Deployment Plan
Quarter 1:
Conduct an integrated analysis and compilation of safety data from various sources, including accident reports, historical crash data, vehicle physics models, and insurance industry reports, focusing on heavy-duty truck underride collisions and near-miss events. Organize meetings with industrial partners to address state-level safety concerns, evaluate trailer underride guard requirements, and explore standards for smart trailers. Identify key data and regulatory gaps that must be addressed to support the deployment of underride protection and smart trailer technologies.
Deliverables:
1. Consolidated multi-source safety dataset.
2. Report highlighting data gaps, regulatory inconsistencies, and industry needs.
3. Initial recommendations for improving trailer safety standards.
Quarter 2:
Develop an exploratory framework to examine factors contributing to underride collisions and near-miss events, utilizing telematics, sensor data, and vehicle dynamics. Engage with stakeholders, including the City of Pittsburgh and Pitt Ohio, to discuss local challenges related to heavy-duty vehicle and trailer safety. Collaborate on strategies for near-miss prevention and the implementation of smart trailer technologies, ensuring alignment with regional transportation goals and safety initiatives.
Deliverables:
1. Exploratory framework for analyzing underride risks.
2. Identification of sensor integration strategies for monitoring near-miss events.
3. Partner agreements for initial field testing locations.
4. Establish a website for the project to allow researchers and practitioners to access the digital twin model developed by the project team
Quarter 3:
Analyze and compile initial results from data analysis and model testing, focusing on safety zone predictions, the effectiveness of underride guards, and real-time monitoring systems. Share preliminary findings with deployment partners, including PennDOT, Pitt Ohio, and the City of Pittsburgh, to validate the results and gather feedback. Refine models and methodologies based on partner input to ensure practical applicability and alignment with local and state needs.
Deliverables:
1. Preliminary model results and performance assessments from initial field tests.
2. Publish a conference or journal paper for distributing the knowledge
3. Industry and regulatory feedback on smart trailer safety interventions.
4. Adjusted roadmap for full-scale deployment in Quarter 4.
Quarter 4:
Deliver actionable outputs, including policy recommendations, technical tools, and sharable algorithms, to academia, industry, government agencies, and community stakeholders. Produce a detailed policy brief for state and local policymakers with recommendations for integrating underride guards, smart trailer technology, and other safety solutions to reduce fatalities. Develop "Truck Maps" to assist entities such as the Department of Mobility and Infrastructure (DOMI) in Pittsburgh with decisions on truck routes, usage regulations, and operational timeframes. Include recommendations for work zones, heavy-duty vehicle operation policies, and technical development and implementation roadmaps for smart trailer technologies.
Deliverables:
1. Final policy recommendations and technical reports.
2. Large-scale safety intervention roadmap for fleets.
3. Public-facing "Truck Maps" to support local transportation planning.
Expected Outcomes/Impacts
Comprehensive Evaluation of Smart Trailer Systems. A detailed assessment of existing smart trailer technologies, identifying their capabilities, limitations, and integration challenges within current fleet operations. This evaluation will provide actionable insights to optimize the deployment of smart trailer systems in various operational environments.
Standardized Definition of Smart Trailers. Development of a clear, industry-accepted definition of “smart trailers,” establishing benchmarks for technology integration, performance metrics, and safety standards. This definition will serve as a foundation for future advancements in smart trailer technologies and regulatory alignment.
Data and Simulation Model Documentation. Thorough documentation of collected data and simulation models, including methodologies for assessing safety benefits and economic implications of implementing side and rear underride guards. These resources will offer evidence-based insights for stakeholders, enabling data-driven decision-making regarding trailer safety investments.
Predictive Simulation Models for Safety and Cost-Benefit Analysis. Development of simulation models that evaluate vehicle operations in various transportation scenarios, predicting the safety outcomes and cost-benefit implications of deploying underride guards. These models will support policymakers and industry leaders in making informed decisions about trailer safety solutions.
Policy Recommendations and Technical Tools. A policy brief with recommendations for state and local policymakers on integrating underride guards, smart trailer technology, and additional safety solutions to reduce fatalities and improve operational efficiency. Deliverables will include actionable policy suggestions, such as “Truck Maps,” to assist government agencies like Pittsburgh’s Department of Mobility and Infrastructure (DOMI) in optimizing truck routes, usage regulations, and operational timeframes, along with work zone guidelines and technical implementation roadmaps.
Strengthened Industry Partnerships. Enhanced collaboration with industry stakeholders, including strengthened partnerships with Clarience Technologies and Safety Emissions Solutions, and new relationships with Pitt Ohio, RIDC, and the Trade Institute of Pittsburgh (TIP). These partnerships will facilitate knowledge exchange, adoption of best practices, and the practical implementation of smart trailer technologies.
Training of Future Experts. Contribution to workforce development by training Ph.D., master’s, and undergraduate students in advanced smart trailer systems, data analysis, and safety policy research, preparing the next generation of experts to tackle critical transportation challenges.
Expected Outputs
Development of a standardized framework defining the essential components and capabilities that constitute a 'smart trailer,' establishing industry benchmarks for technology integration and performance.
Creation of physics-informed machine learning models capable of simulating vehicle dynamics and predicting safety outcomes under various scenarios, aiding in the assessment of underride guard effectiveness.
Design and implementation of a platform that consolidates data from accident reports, vehicle physics models, highway design specifications, regulatory documents, historical crash data, and insurance reports, facilitating comprehensive analysis and informed decision-making.
Formulation of evidence-based guidelines and recommendations for policymakers regarding the implementation and design of side and rear underride guards, supported by rigorous cost-benefit analyses. Additionally, this project will collaborate with national workforce and industry stakeholders to establish a standardized definition of ‘smart trailer,’ ensuring alignment with safety and operational standards, while fostering a skilled workforce capable of advancing smart trailer technology.
TRID
A review of TRID literature reveals extensive work on smart trailer technologies and safety systems for heavy-duty vehicles. Key studies focus on underride guard effectiveness, predictive maintenance, advanced control frameworks, and crash-prevention technologies like adaptive cruise control (ACC) and stability systems. These studies highlight the potential of underride guards to mitigate side and rear collisions and the effectiveness of side view assist, forward collision warning, and roll stability systems in reducing crashes. Despite these advancements, existing cost-benefit analyses for underride guards and crash-prevention technologies rely heavily on underreported data and lack robust modeling frameworks. Moreover, while underride guards provide a passive solution, integrating vision-based early warning systems can actively prevent collisions by alerting operators and nearby vehicles, complementing guard effectiveness. Vision systems, coupled with telematics and AI, offer a dynamic approach to collision avoidance and real-time monitoring. This project builds on prior work by introducing an integrated framework that combines physics-informed machine learning, predictive AI, and telematics data. It advances the state of the art by evaluating both passive and active safety systems and their cost-benefit impacts. Collaboration with industry stakeholders ensures real-world validation, bridging research gaps and setting a foundation for informed regulatory standards and operational safety practices.
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| jfleisch@andrew.cmu.edu |
Fleischman, Jane |
Carnegie Mellon University |
Other |
Student - Undergrad |
| avaj@andrew.cmu.edu |
Jahan Biglari, Ava |
Carnegie Mellon University |
Other |
Student - PhD |
| ebodnar@andrew.cmu.edu |
Kean, Emily |
Carnegie Mellon University |
Other |
Staff - Business Manager |
| ptang@andrew.cmu.edu |
Tang, Pingbo |
Carnegie Mellon University |
PI |
Faculty - Tenured |
| meitao@andrew.cmu.edu |
Tao, Mei |
Carnegie Mellon University |
Other |
Staff - Business Manager |
| pyzhang@cmu.edu |
Zhang, Peter |
Carnegie Mellon University |
Co-PI |
Faculty - Untenured, Tenure Track |
Budget
Amount of UTC Funds Awarded
$90000.00
Total Project Budget (from all funding sources)
$200000.00
Documents
Match Sources
No match sources!
Partners
| Name |
Type |
| Regional Development Corporation (RIDC) |
Deployment Partner Deployment Partner |
| PITT OHIO |
Deployment Partner Deployment Partner |
| Clarience Technologies, LLC |
Deployment Partner Deployment Partner |
| Safety Emissions Solutions, LLC |
Deployment Partner Deployment Partner |
